Objectives of this study

The mental health of university students is an area of increasing concern worldwide. Although some recent studies revealed a high prevalence of depression and anxiety among Bangladeshi university students, no study has yet investigated the link between marriage pressure and these common psychological disorders and their effects on career and goal setup. Therefore, aims of these study is-

  • To determine the state of marriage pressure on female undergraduate students -To determine the association between mental health and marriage pressure.
  • To determine how marriage pressure affects education, career, and goal setup.
  • To determine the influencing factors behind marriage pressure

Descriptive Statistics - Frequency analysis

library(readxl)
data <- read_excel("F:/Fiverr/up-work project/dataset/project 68.xlsx")
tbl<- knitr::kable(table(data$MarriagePressure),col.names = c("Marriage Pressure", "Count"), "simple")
tbl
Marriage Pressure Count
no 595
yes 341 
library(plotly)
fig <- plot_ly(data, labels = ~MarriagePressure, values = ~frequency(MarriagePressure), type = 'pie')

fig <- fig %>% layout(title = 'Marriage Pressure of Female Students',
                      
                      xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
                      
                      yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
                      width = "50%", 
                      height = 450)

fig

A total of 936 participants responded to the survey. Of these, 595 participants answered “No” to the question about having marriage pressure, while 341 participants answered “Yes.”

The results of the survey indicate that the majority of participants 63.6% answered “No” to the question about having marriage pressure. Only 36.4% participants answered “Yes.” This data suggests that a larger proportion of the participants did not experience marriage pressure, while a smaller proportion reported that they did. The pie chart provides a clear and straightforward representation of the distribution of responses and highlights the differences between the two categories.

When we look after another variable PHQ-9 (Patient Health Question) the scores were then analyzed to categorize the students into different levels of depression severity, including normal depression, mild depression, moderate depression, moderately severe depression, and severe depression.

Depression Severity among students

phq_categories<- ifelse(data$phq9 <= 4, "Normal",
       ifelse(data$phq9 <= 9, "Mild", 
              ifelse(data$phq9 <= 14,"Moderate",
                     ifelse(data$phq9 <= 19, "Moderately Severe", 
                            ifelse(data$phq9 <=27,"Severe",data$phq9)))))


phq_categories <- table(phq_categories)
phq_categories_df <- as.data.frame(phq_categories, stringsAsFactors = FALSE)
colnames(phq_categories_df) <- c("Depression Severity", "Frequency")
knitr::kable(phq_categories_df, col.names = c("Depression Severity", "Frequency"), "simple")
Depression Severity Frequency
Mild 248
Moderate 224
Moderately Severe 136
Normal 272
Severe 56 
library(plotly)
fig <- plot_ly(phq_categories_df, x = ~`Depression Severity`, y = ~Frequency, type = "bar", color = ~`Depression Severity`) %>% 
  layout(title = "Depression Severity Categories", xaxis = list(title = "Category"), yaxis = list(title = "Frequency"))

fig